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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

共 68649 条记录，以下是4741-4750 订阅

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ARTIFICIAL INTELLIGENCE ASSISTED SERVICE MARKETING USING DEEP ASSISTED NEURAL NETWORK

ARTIFICIAL INTELLIGENCE ASSISTED SERVICE MARKETING USING DEE...

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2023 IEEE international conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Shanthana, S. Gopal, Vidhya Navulla, Durgaprasad Seal, Sudeshna Sohail, Mohammed Ali Paul, Gunti Anil Rajalakshmi Institute of Technology Tamilnadu Chennai India Department of Computer Science and Engineering Sona College of Technology Tamilnadu Salem India Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India OmDayal Group of Institutions West Bengal Uluberia India Department of Computer and Network Engineering College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia Department of Mechanical Engineering Karunya Institute of Technology and Sciences Tamilnadu Coimbatore India

ISBN: (纸本)9798350305708

Improving marketing abilities, the quality of one product, and one connections with one target audience are at the service marketing approach. The marketing of goods and services can both be considered to come within the scope of this category. This data is stored in a centralized location in these databases for ease of access and analysis. Based on the findings of this study, AI has played a crucial role in big data analytics, making it possible to foresee and provide guided experiences that are individualized based on the needs of customers. In this paper, we develop a AI assisted customer support system to deliver experiences that result in advocates and consumers that remain loyal over time, the usage of AI and predictive analytics is crucial. © 2023 IEEE.

关键词： artificial intelligence neural network Service marketing validation

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Enhancing Local Descriptor Networks for Few-Shot Image Classification

Enhancing Local Descriptor Networks for Few-Shot Image Class...

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2023 international Annual conference on Complex Systems and Intelligent science, CSIS-IAC 2023

作者： Dou, Quansheng Li, Xiujuan Tang, Huanling Liu, Yanchao Zhou, Liyuan College of Computer Science and Technology Shandong Technology and Business University Yantai China College of Computer Science and Technology Kashi University Xinjiang China College of Information and Electronic Engineering Shandong Technology and Business University Yantai China

ISBN: (纸本)9798350309003

Deep learning models rely on a large amounts of annotated data, and the performance of the model is unsatisfactory when the labeled samples are scarce. Existing few-shot learning methods based on local descriptors fail to consider correlation and usefulness between local descriptors. At the same time, due to the limited perceptual fields, it cannot make full use of the global information generated by distant pixels, leading to the problem of extracting features that are highly similar due to the same background, which in turn makes the model misclassify. In order to solve these problems, Fusioning Local and Global features Deep Nearest Neighbor Neural Network is proposed, which is gather relevant features through clusters of local descriptors and synthesize local and global information to learn more discriminative features, so as to further improve the classification performance of few-shot data. The experimental results in this paper show that the method shows superiority on miniImagNet, tieredImageNet and CUB-200-2011 datasets. © 2023 IEEE.

关键词： Image classification

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Enhancing PCOS Prediction: A System based on Ensemble Machine Learning Techniques 9

Enhancing PCOS Prediction: A System based on Ensemble Machin...

引用

9th IEEE international Women in Engineering (WIE) conference on Electrical and computer Engineering, WIECON-ECE 2023

作者： Khan, Moriom Nila, Farzana Tabasaum, Nafisa Suha, Sayma Alam Islam, Muhammad Nazrul Bangladesh University of Professionals Department of ICT Dhaka Bangladesh Bangladesh Army International University of Science & Technology Department of Computer Science & Engineering Cumilla Bangladesh Military Institute of Science and Technology Department of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350319651

Polycystic Ovarian Syndrome (PCOS) has become the most common hormonal disease in women who are in their reproductive age. PCOS in women affects the reproductive organs that generate the hormones progesterone and estrogen, which regulate the menstrual cycle. Although there are not that many symptoms in the early stage of PCOS, as the condition continues, they become much more obvious, and at that point, the treatment becomes much more difficult. Again, the Machine Learning (ML) model can help to predict PCOS at the early stage. Therefore, the main objective of this research is to develop an ensemble-based machine learning model for predicting PCOS and to develop a web system based on the ensemble model for predicting PCOS. To achieve these objectives, best set of ML algorithms were selected by exploring performances (accuracy and false negative error) of ten ML algorithms were explored. An ensemble stacking classifier was proposed by integrating the six classifiers that showed an accuracy of 93.74% for predicting the PCOS. A web system was also developed adopting the proposed ensemble model for predicting PCOS by the end users. © 2023 IEEE.

关键词： Medical informatics

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4th international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2023

作者： Luo, Senzi Xiao, Jiayin School of Automation Guangdong University of Technology Guangdong China

ISBN: (纸本)9798350343113

Fine-grained action recognition poses a significant challenge as it involves distinguishing subtle and distinctive motion variations within fine-grained action categories. Previous methods have improved performance by enhancing the network's ability to capture variations in the action space and temporal changes. However, they face limitations in effectively distinguishing subtle differences within actions when they involve varying numbers of repetitions. In this paper, we propose an effective module called the Self-similarity Attention Module (SAM). This module represents the self-similarity of actions using the Temporal Self-similarity Matrix (TSM) and utilizes channel-wise excitation to capture the periodicity information in actions. The Self-similarity Attention Module can be embedded into any 3D convolutional neural network. Our approach outperforms previous skeleton-based action recognition methods on the wildly used FineGym dataset, which confirms its effectiveness and efficiency. © 2023 IEEE.

关键词： Musculoskeletal system

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Research on Knowledge Fusion Optimization System Based on computer Large-Scale Heterogeneous Network Big Data

Research on Knowledge Fusion Optimization System Based on Co...

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2023 IEEE international conference on Sensors, Electronics and computer Engineering, ICSECE 2023

作者： Xiaogan, Chen Peng, Shu Xiaohan, Lai Jiabin, Huang Qing, Nie State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China Yingda Media & Investment Group Co. Ltd. Beijing China

ISBN: (纸本)9798350313734

Network big data refers to the big data generated by the interaction and fusion of "people, machines and things"in the network space and available on the Internet. In this paper, from the perspective of ontology-based knowledge representation, network knowledge is represented in the form of five-tuples such as instance, attribute, domain set, relation and concept. Then, from the perspective of complex system, the new research perspective of deep learning technology and the new technology trends of grain computing in information fusion are analyzed, and the feasibility of the integration and expansion of grain computing and deep learning is speculated. A new overlapping community detection algorithm based on modularity optimization is designed. It is found that the framework can solve the dynamic update of network big data and the integration and reorganization of existing professional knowledge base to a certain extent. It can effectively guarantee the construction of domain knowledge base and improve its coverage and timeliness. © 2023 IEEE.

关键词： Heterogeneous networks

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CONVOLUTIONAL NEURAL NETWORK for MULTICLASS SKIN CANCER IMAGE CLASSIFICATION

CONVOLUTIONAL NEURAL NETWORK for MULTICLASS SKIN CANCER IMAG...

引用

2023 international conference on computer Communication and Informatics, ICCCI 2023

作者： Ramya, J. Vijaylakshmi, H.C. Saifuddin, Huda Mirza JSS Science and Technology University Mysuru Computer Science and Engineering Mysore India

ISBN: (纸本)9798350348217

Skin cancer is the most dangerous, having one-third diagnosed rate in worldwide. It is supposed to spread vastly to other body parts when it is not detected at beginning stage. Skin cancer diagnosing system contains three major steps such as segmentation, feature extraction and classification. Firstly, images are pre-processed using 2D median filter to remove some artifacts like thin hair, gel, air bubble etc. followed by segmentation using discrete wavelet transform to extract lesion region from background skin region is described in our last paper. In this paper, we have passed previously segmented images to Convolutional Neural Network (CNN) for feature learning and classification. Experimentation conducted on three classes of PH2 dataset, classification results are satisfactory and accurate. © 2023 IEEE.

关键词： Classification (of information)

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Towards Semantic Data Management Plans for Efficient Review Processing and automation 13

Towards Semantic Data Management Plans for Efficient Review ...

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13th international conference on Data science, technology and Applications, DATA 2024

作者： Martínková, Jana Suchánek, Marek Faculty of Information Technology CTU in Prague Prague Czech Republic

ISBN: (纸本)9789897587078

In recent times, Data Management Planning has become increasingly crucial. Effective practices in data management ensure more precise data collection, secure storage, proper handling, and utilization beyond the primary project. However, existing DMPs often suffer from complex structures that impede accessibility for humans and machines. This project aims to address these challenges by converting DMPs into formats that are both machine-actionable and human-readable. Leveraging established DMP templates and relevant ontologies, our methodology involves analyzing diverse approaches to achieve this dual functionality. We assess machine-actionability through comparative evaluations using AI and NLP tools. Furthermore, we identify gaps in ontologies, laying the groundwork for future enhancements in this critical area of research. © 2024 by SCITEPRESS – science and technology Publications, Lda.

关键词： Ontology

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Design and Implementation of Minkowski Feature Selection for Machine Learning Techniques 5

Design and Implementation of Minkowski Feature Selection for...

引用

5th international conference on Information technology, Applied Mathematics and Statistics, ICITAMS 2023

作者： Hakim, Ekram Al-Shammary, Dhiah Mahdi, Ahmed M. University Of Al-Qadisiyah College Of Computer Science And Information Technology Dep. Computer Science Diwaniyah Iraq

ISBN: (纸本)9798350335118

This paper proposes a design and implementation of a highly efficient feature selection based on Minkowski's mathematical similarity for machine learning. Moreover, the development of the Minkowski feature selection can be general for intelligent datasets and machine learning. Intelligent optimizers and feature selection represent an advanced stage in machine learning and innovative computer applications by reducing the overall number of features to be included in the classification function process. This paper analyses Minkowski's selection of best factors for EEG Bonn University data. Minkowski cancels the random movement and systematically limits the number of features and its point-to-point movement in order to promote exploration and reduce the probability of falling into stagnation. The main measures to analyze this research are Accuracy, precision, Recall, and F1score assessment. Moreover, the accuracy of Minkowski's selection provided better classification results than some of the other approaches (100%) © 2023 IEEE.

关键词： Feature Selection

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Capacitor-Current PID Feedback Control Method for Grid-Connected Inverter to Improve System Stability and Harmonic Rejection Ability 3

Capacitor-Current PID Feedback Control Method for Grid-Conne...

引用

3rd IEEE international conference on Power, Electronics and computer Applications, ICPECA 2023

作者： Xie, Cui Wang, Jianze Xie, Jiakun Guo, Yue Liu, Xu School of Electrical Engineering & Automation Harbin Institute of Technology Harbin China

ISBN: (纸本)9781665472784

The wide variation of the grid impedance can easily cause system resonance or even system instability of LCL-type grid-connected inverter in weak grid, and the injected grid current is severely distorted due to grid voltage harmonics. To address this issue, a capacitor-current PID feedback control method is proposed in this paper to correct inverter system impedance. It makes the output impedance of inverter and the equivalent virtual impedance of the filter capacitor in parallel have a nonnegative real part within the Nyquist frequency, while increasing the inverter output impedance's low-frequency gain. Then, the design guideline for the parameters of capacitor-current PID feedback coefficient is given. The proposed method has the ability to significantly enhance system stability and harmonic suppression capability, simultaneously enhancing system robustness to variation in grid impedance. Finally, simulation results verify the effectiveness of the proposed method and parameter design. © 2023 IEEE.

关键词： System stability

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Next-Generation IoT Intrusion Detection: Leveraging Hybrid Deep Learning Algorithms for Enhanced Security 2

Next-Generation IoT Intrusion Detection: Leveraging Hybrid D...

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2nd international conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Urvashi Agarwal, Parul Raghuvanshi, Kamlesh Kumar Ahmed, Jawed Department of Computer Science and Engineering Jamia Hamdard University Delhi India Department of Computer Science University of Delhi Delhi India

ISBN: (纸本)9798331530389

Wireless sensor node and DDoS (Distributed Denial of Service) is a major attack way that can exhaust the energy of a sensor, disrupt the communication traffic of an IoT network. We must be on the lookout for these attacks. With the quantity of sensor nodes, traditional methods face difficulty in detecting network traffic analysis requiring big data analysis. This paper is dedicated to Network Traffic analysis using Big Data environment and detecting attacks using Deep Learning. Implemented with PySpark in Google Colab, using Keras and Scikit-Learn built-in libraries. To be more specific, training and testing are conducted on the 'CICIoT2023'/'TON IoT' datasets, respectively. When we use a correlation feature reducer, it virtually considers the most significant features for inclusion and reduces all of our features. The first contains a hybrid deep leaning model combining one-dimensional EfficientDet and Gradient Boosting Machine (GBM), comparing it with ten other algorithms. It gen- erated 99.95% binary classification accuracy for the CICIoT2023 dataset and 99.75% for TON IoT data. © 2025 IEEE.

关键词： Network intrusion

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